shreejalmaharjan-27/waifu2x:edb4c6d8 – Run with an API on Replicate

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shreejalmaharjan-27 /waifu2x:edb4c6d8

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=shreejalmaharjan-27/waifu2x

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run shreejalmaharjan-27/waifu2x using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "shreejalmaharjan-27/waifu2x:edb4c6d870c62d0f9343b1eaa6fef3350b5cc85b01ac99244af15446fd6df603",
  {
    input: {
      file: "https://replicate.delivery/pbxt/LJwD9PIuraXnwl2qCiQdxP0G8HCCtpwQTsPxQwM6fnVYRtju/5c867f1b339047cca926d134f3a5afba259f3653f6a548ecf20b57ef077140bc.jpg",
      tile_size: 256,
      upscaling: "4x",
      batch_size: 4,
      model_type: "art",
      noise_level: 3,
      transparency: false
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run shreejalmaharjan-27/waifu2x using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "shreejalmaharjan-27/waifu2x:edb4c6d870c62d0f9343b1eaa6fef3350b5cc85b01ac99244af15446fd6df603",
    input={
        "file": "https://replicate.delivery/pbxt/LJwD9PIuraXnwl2qCiQdxP0G8HCCtpwQTsPxQwM6fnVYRtju/5c867f1b339047cca926d134f3a5afba259f3653f6a548ecf20b57ef077140bc.jpg",
        "tile_size": 256,
        "upscaling": "4x",
        "batch_size": 4,
        "model_type": "art",
        "noise_level": 3,
        "transparency": False
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run shreejalmaharjan-27/waifu2x using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "shreejalmaharjan-27/waifu2x:edb4c6d870c62d0f9343b1eaa6fef3350b5cc85b01ac99244af15446fd6df603",
    "input": {
      "file": "https://replicate.delivery/pbxt/LJwD9PIuraXnwl2qCiQdxP0G8HCCtpwQTsPxQwM6fnVYRtju/5c867f1b339047cca926d134f3a5afba259f3653f6a548ecf20b57ef077140bc.jpg",
      "tile_size": 256,
      "upscaling": "4x",
      "batch_size": 4,
      "model_type": "art",
      "noise_level": 3,
      "transparency": false
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/shreejalmaharjan-27/waifu2x@sha256:edb4c6d870c62d0f9343b1eaa6fef3350b5cc85b01ac99244af15446fd6df603 \
  -i 'file="https://replicate.delivery/pbxt/LJwD9PIuraXnwl2qCiQdxP0G8HCCtpwQTsPxQwM6fnVYRtju/5c867f1b339047cca926d134f3a5afba259f3653f6a548ecf20b57ef077140bc.jpg"' \
  -i 'tile_size=256' \
  -i 'upscaling="4x"' \
  -i 'batch_size=4' \
  -i 'model_type="art"' \
  -i 'noise_level=3' \
  -i 'transparency=false'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/shreejalmaharjan-27/waifu2x@sha256:edb4c6d870c62d0f9343b1eaa6fef3350b5cc85b01ac99244af15446fd6df603
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "file": "https://replicate.delivery/pbxt/LJwD9PIuraXnwl2qCiQdxP0G8HCCtpwQTsPxQwM6fnVYRtju/5c867f1b339047cca926d134f3a5afba259f3653f6a548ecf20b57ef077140bc.jpg",
      "tile_size": 256,
      "upscaling": "4x",
      "batch_size": 4,
      "model_type": "art",
      "noise_level": 3,
      "transparency": false
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

7.7 seconds

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